class_docstr: Segmentation loss as cross-entropy for raw unnormalized scores given ground truth
  labels. Segmentation ground truth labels are defined for the bounding box of
  interest at some fixed resolution [S, S], where
      S = MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE.
docstr: Fake segmentation loss used when no suitable ground truth data
  was found in a batch. The loss has a value 0 and is primarily used to
  construct the computation graph, so that `DistributedDataParallel`
  has similar graphs on all GPUs and can perform reduction properly.
  
  Args:
      densepose_predictor_outputs: DensePose predictor outputs, an object
          of a dataclass that is assumed to have `coarse_segm`
          attribute
  Return:
      Zero value loss with proper computation graph
docstr_tok: ["Fake","segmentation","loss","used","when","no","suitable","ground","truth","data","was","found","in","a","batch",".","The","loss","has","a","value","0","and","is","primarily","used","to","construct","the","computation","graph",",","so","that","`","DistributedDataParallel","`","has","similar","graphs","on","all","GPUs","and","can","perform","reduction","properly",".","Args",":","densepose_predictor_outputs",":","DensePose","predictor","outputs",",","an","object","of","a","dataclass","that","is","assumed","to","have","`","coarse_segm","`","attribute","Return",":","Zero","value","loss","with","proper","computation","graph"]
code: def fake_value(
      self, densepose_predictor_outputs: Any
  ) -> torch.Tensor:
      """
      Fake segmentation loss used when no suitable ground truth data
      was found in a batch. The loss has a value 0 and is primarily used to
      construct the computation graph, so that `DistributedDataParallel`
      has similar graphs on all GPUs and can perform reduction properly.
  
      Args:
          densepose_predictor_outputs: DensePose predictor outputs, an object
              of a dataclass that is assumed to have `coarse_segm`
              attribute
      Return:
          Zero value loss with proper computation graph
      """
      return densepose_predictor_outputs.coarse_segm.sum() * 0
  
file_path: detectron2/projects/DensePose/densepose/modeling/losses/segm.py
imports: from typing import Any, List
  import torch
  from torch.nn import functional
  from detectron2.config import CfgNode
  from detectron2.structures import Instances
  from .utils import resample_data
label_desc: Use image node_id 3 for calling the SegmentationLoss obj's underlying member method code with example usage: obj.fake_value(densepose_predictor_outputs) and returns: unknown